October 2025, From the Field
Equity markets were strong this quarter, led by continued enthusiasm for artificial intelligence (AI) and the resumption of stimulative monetary policy. Factor performance was led by high‑risk, low‑quality, and less profitable securities (Figure 1). This pattern resembles what we observed in the final three months of last year and the second quarter of 2025 but was more extreme. The preference for low quality and risk seeking occurred across the regions we track, except for Pacific ex‑Japan. However, these trends were most significant in the U.S., especially in small caps, and are the focus of the rest of our newsletter.
Past performance is not a guarantee or a reliable indicator of future results. Sources: Refinitiv/IDC data, Compustat, Worldscope, Russell, and MSCI. Analysis by T. Rowe Price. See Additional Disclosures. Total return data are in U.S. dollars. Factor returns are calculated as equal‑weighted quintile spreads.
Factors or factor analysis involves targeting quantifiable firm characteristics, or “factors,” that can explain differences in stock returns. Over the last 50 years, academic research has identified hundreds of factors that impact stock returns. See Appendix for calculation methodology, definitions of financial terms, and more details on the factors referenced throughout the article. The data presented in this material are for illustrative purposes only and do not represent an actual investment nor any T. Rowe Price product.
Given the extreme outperformance of high‑risk and low‑quality stocks, we wanted to examine this market environment in the context of behavioral finance.
Past performance is not a guarantee or a reliable indicator of future performance. The thematic baskets depicted are not representative of an actual investment or portfolio, and the baskets cannot be invested into. Actual investment results associated with these themes would differ, perhaps significantly. See Methodology Disclosure for additional important information. Source: FactSet. Analysis by T. Rowe Price. Each thematic basket was created by identifying representative stocks with clear exposure to the theme. They are capitalization weighted and are not rebalanced during this period. Total return data are in U.S. dollars. Excess return is the difference between a thematic basket’s return and the Russell 2500 Index’s return.
Prospect theory holds that investors tend to overestimate the probabilities of positively skewed, lottery‑like payoffs. These tendencies underpin a preference for high‑risk stocks with high potential rewards, leading to these securities being overpriced, on average, and resulting in poor forward returns.
Three phenomena are exacerbating this effect:
The emergence of a potentially transformational technology like AI can produce an unusually wide dispersion of outcomes. Many firms will resemble the short‑lived Pets.com; only a few will become the next Amazon.com.
This breadth of possibilities increases the appeal of lottery ticket‑like narratives. As the perception of AI’s capabilities grows, the imagined jackpot grows as well, drawing in investors and driving price appreciation. The result is a reflexive loop in which rising prices reinforce conviction in the stock or theme, attracting more capital. Investors may also start treating stocks as options, where perceived fair value rises with volatility.
Importantly, because the true winners and losers from a major innovation wave may not be known for years, these lottery ticket‑like stocks tend to trade more on sentiment and narrative than on present‑day business fundamentals.
The result is greater demand for speculative, high‑volatility stocks, a scenario that reflects the factor returns from the past 12 months.
We see signs of this speculative impulse in some of the themes that have fired investors’ imaginations. Stocks with exposure to quantum computing, cryptocurrency, space technology, the AI buildout, and AI energy demand have greatly outperformed this year, particularly in the third quarter (Figure 2).
Prospect theory suggests that long‑term investors should lean the other way. Highly speculative, lottery ticket‑like stocks tend to be overpriced, and the more exuberant investors become, the more overpriced these stocks are likely to be.
It’s tempting to chase these stocks, as some of the biggest winners have come from this group in the past. The mega‑cap internet and technology companies, for example, looked extraordinarily expensive early in their lifecycles, before the transformative nature of their products became clear. It’s also tempting to avoid these lottery tickets completely, as small shifts in sentiment, which is notoriously fickle, can translate into violent price swings and expensive valuations.
We think the best practice is to stay meaningfully underweight highly speculative stocks while diligently managing risk and respecting the possibility that, over time, some future winners may emerge from this cohort. We also favor a “basket approach,” believing that diversified portfolios are better equipped to handle this environment.
After significant high‑risk, low‑quality rallies in three of the last four quarters, the most common questions we’re getting from clients are how expensive risk looks and whether quality is attractive.
Our valuation work suggests that the potential risk/reward setup in high‑quality stocks varies meaningfully with market capitalization:
What’s behind these differences in the appeal of quality and risk in U.S. small and large caps?
In small caps, lower‑quality, speculative stocks exposed to popular themes have led the way and trade at valuation premiums that are harder to justify.
Quantum computing stocks, for example, are up more than 450% since the U.S. election (Figure 2), even though many in the field believe that this emerging technology is still five‑plus years away. The pure‑play leaders in quantum computing could be unprofitable for some time. Similarly, companies involved with space technology have rallied significantly over the past year despite unproven business models and regulatory hurdles.
We are confident in our views on quality and risk. At the same time, it’s important to remain humble in our certainty about the future at a time when massive technological change can widen the dispersion of outcomes.
In both the large‑cap Russell 1000 Index and the small‑cap Russell 2000 Index, high‑risk stocks’ returns outstripped their low‑risk counterparts by a wide margin over the past 12 months. Relatedly, high‑quality stocks underperformed low quality (Figure 3).
Past performance is not a guarantee or a reliable indicator of future results.
Sources: Refinitiv/IDC data, Compustat, and Russell. Analysis by T. Rowe Price. Total return data are in U.S. dollars. Factor returns are calculated as equal‑weighted quintile spreads.
Going back to the end of 1990, this 12‑month risk rally was an 87th percentile outcome for U.S. large caps and a 90th percentile scenario for U.S. small caps.1
Given the magnitude of the moves in high‑risk and low‑quality U.S. large and small caps, the obvious questions are whether risk has become too expensive, and whether quality appears attractive.
“Going back to the end of 1990, this 12-month risk rally was an 87th percentile outcome for U.S. large caps and a 90th percentile scenario for U.S. small caps.1”
Last quarter we wrote about high quality appearing expensive within U.S. large cap, a dynamic we suggested might resolve itself through a market broadening.
As shown in Figure 1, quality significantly underperformed over the subsequent three months. This partial correction somewhat moderated the expensiveness of high‑quality U.S. large caps (Figure 4).
Sources: Refinitiv/IDC data, Compustat, Thomson/IBES, and Russell. Analysis by T. Rowe Price. The plot points represent the Z‑score of our proprietary multifactor metric indicating whether the highest quintile (reconstituted monthly) of quality or risk is cheap or expensive. The numbers correspond to the number of standard deviations (e.g., a reading of 1 means “1 standard deviation cheap”).
Quality remains somewhat expensive in U.S. large caps, but it may be warranted because profitability and other fundamentals are stronger than ever. Also, valuations for high‑quality U.S. large caps have trended higher over the past decade and a half; within this context, the cohort may even be somewhat fairly valued.
In contrast to U.S. large caps, we see a dislocation emerging in U.S. small caps, particularly in the growth segment (Figure 5A and Figure 5B).
Sources: Refinitiv/IDC data, Compustat, Thomson/IBES, and Russell. Analysis by T. Rowe Price. The plot points represent the Z‑score of our proprietary multifactor metric indicating whether the highest quintile (reconstituted monthly) of quality or risk is cheap or expensive.
Sources: Refinitiv/IDC data, Compustat, Thomson/IBES, and Russell. Analysis by T. Rowe Price. The graph depicts the capitalization-weighted distribution of durable growth companies in the top 2 quintiles for high risk in the large‑cap Russell 1000 Index and the small‑cap Russell 2000 Index.
Given the different starting points and different composition of risk, it makes sense that valuations for quality and risk would become more dislocated in U.S. small caps than in the U.S. large‑cap segment.
In U.S. large cap, high‑quality stocks still appear expensive. Given the strong fundamentals in this cohort, we think the mispricing is less significant than the data suggest. Still, we believe it makes sense to consider tilting away from expensive high‑quality large caps in favor of a broadening positioning.
In U.S. small cap, the data are unequivocal. Quality looks cheap, and risk is expensive—historically so in small‑cap growth. The narrative fits the data, as many of the lottery ticket‑like stocks offering exposure to quantum computing, space technology, and other popular themes reside in the small‑cap growth universe. For long‑term investors, this scenario suggests a strong tilt toward quality and away from risk, which is the strategic positioning for many small‑cap managers.
Still, it’s important to remain humble and consider countervailing evidence and arguments. Dislocated valuations do not necessarily mean that a correction is imminent. We must respect that today’s dynamics could allow these imbalances to persist in the near term.
Here are three considerations for small-cap investors:
Past performance is not a guarantee or a reliable indicator of future results. Sources: NYSE, Nasdaq, Datastream, IDC data, IHS Markit, and Russell. Analysis by T. Rowe Price. Short interest factor volatility is measured using the trailing 3‑year rolling standard deviation of equal‑weighted monthly excess returns, annualized. Excess returns are total returns (with extreme values capped at 1% tails) for the 10% of the Russell 2000 Index with the highest short interest minus the equal‑weighted index’s return. Underlying returns data start June 30, 2001. The cohort of highly shorted stocks is reconstituted monthly.
Sources: Barra MSCI, IHS Markit, and Russell. Analysis by T. Rowe Price. The cohort of highly shorted stocks is reconstituted monthly and comprises the 5% of the Russell 2500 Index with the highest short interest. Barra beta measures a stock’s potential sensitivity to broad market movements. It is calculated using excess returns regression coefficient to capitalization-weighted universe returns over the trailing 252 trading days. Data are updated monthly.
Sources: Barra MSCI, IHS Markit, and Russell. Analysis by T. Rowe Price. The cohort of highly shorted stocks is reconstituted monthly and comprises the 5% of the Russell 2500 Index with the highest short interest. Barra earnings yield reflects a company’s earnings relative to its share price and serves as a proxy for valuation. This metric combines trailing earnings yield, forward earnings yield, and operating cash flow to enterprise value. Data are updated monthly.
Bottom line: Although we favor tilting toward quality and away from risk in small caps, we expect continued volatility along the way. We favor an approach that manages the risk in these high‑volatility thematic stocks at three levels: stock‑specific risk, theme‑level risk, and aggregate exposure to thematic risk within the portfolio.
Many clients have asked how quality and risk are priced today after an extremely strong 12 months for high‑risk and low‑quality stocks. Our data suggest different answers in U.S. large and small cap.
Large caps started from a position of high quality being historically expensive; even after recent market movements, we still think quality is somewhat expensive. We recognize the historically strong fundamentals of these companies and see the potential for quality to underperform slightly if the market were to broaden.
In the small‑cap space, risk looks expensive—historically so in small‑cap growth—and quality looks cheap. These dislocations create a larger‑than‑usual opportunity for long‑term investors in quality small caps. But given structural changes in the market, we expect the quality/risk trade‑off to remain volatile—investors will need to be attentive to this risk and manage accordingly. We also believe that taking a “basket approach” within diversified portfolios is better suited for this environment than concentrated portfolios.
Factors are our internally constructed metrics, defined as follows:
Valuation: Proprietary composite of valuation metrics based on earnings, sales, book value, and dividends. Specific value factor weighting may vary by region and sector.
Growth: Proprietary composite of growth metrics based on historical and forward‑looking earnings and sales growth. Factor selection and weighting vary by region and industry.
Momentum: Proprietary measure of medium‑term price momentum.
Quality: Proprietary measure of quality based on fundamental and stock price stability; balance sheet strength; and measures of profitability, capital usage, and earnings quality.
Profitability: Return on equity.
Risk: In this paper, risk is the standard deviation of trailing 12‑month returns. High‑risk stocks exhibit higher standard deviations, indicating a wider degree of variation or dispersion in their historical return.
Size: Market capitalization (positive return means larger stocks outperform smaller stocks).
Durable growth: Our durable growth factor is a proprietary blend of 5 fundamental metrics related to growth, earnings stability, profitability, and balance sheet.
Short interest: We define short interest with the Markit Short Interest factor. Short interest is calculated as the total share of stock on loan divided by free float shares.
Quintile spread: Also referred to as long‑short returns, a quintile spread is calculated by sorting securities based on a specific characteristic or factor criterion, dividing them into five groups (or quintiles), equal‑weighting the securities within each quintile, and then subtracting the bottom‑quintile returns (lowest 20%) from the top‑quintile returns (highest 20%).
Factors and indices cannot be invested in directly and are shown for illustrative purposes only. They do not reflect performance of actual investments nor do they reflect the reduction of fees associated with an actual investment, such as trading costs and management fees.
Other definitions:
Z-score: The quantity of (current score minus the average of previous periods) divided by the standard deviation of previous periods. In this paper, we apply this calculation to our proprietary valuation metric to indicate whether a group of stocks looks expensive or cheap.
Beta: Beta measures the volatility, or risk, of a stock or portfolio relative to the risk of the broad market.
Earnings yield: Earnings yield is a valuation metric that is the inverse of the price‑to‑earnings ratio. It is calculated by dividing a company’s earnings per share by its stock price.
For definitions of certain financial terms, visit https://www.troweprice.com/en/us/glossary.
All investments are subject to market risk, including the possible loss of principal. Past favorable company characteristics may not persist into the future.
Risks: Growth stocks are subject to the volatility inherent in common stock investing, and their share price may fluctuate more than that of income‑oriented stocks. The value approach to investing carries the risk that the market will not recognize a security’s intrinsic value for a long time or that a stock judged to be undervalued may actually be appropriately priced. Small‑cap stocks have generally been more volatile in price than large‑cap stocks. Investing in technology stocks entails specific risks, including the potential for wide variations in performance and usually wide price swings, up and down. Technology companies can be affected by, among other things, intense competition, government regulation, earnings disappointments, dependency on patent protection and rapid obsolescence of products and services due to technological innovations or changing consumer preferences. Investing in cryptocurrency carries a substantial level of risk. It is a nascent asset class that remains largely unregulated, and it is not suitable for all investors.
Methodology Disclosure regarding the T. Rowe Price thematic baskets:
We began actively creating and monitoring thematic baskets in 2024 for research and informational purposes. For each theme, the constituents represent publicly traded U.S. stocks that T. Rowe Price has categorized as companies involved in the relevant theme. All the results shown in the newsletter use the current thematic baskets. Each basket’s constituents are held constant through time for the date ranges shown, and we are showing capitalization-weighted results.
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1 Percentiles rank the 12-month long-short return for risk (Figure 3) relative to periods of the same duration, rolled monthly. Analysis covers December 31, 1990, to September 30, 2025.
2 There is no assurance that historical growth will persist in the future. See Appendix for more details on the durable growth metric.
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